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license: mit
language:
- en
tags:
- gesture-recognition
- sensor-data
- flex-sensors
- accelerometer
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: label
dtype: string
- name: batch
list:
list: int64
splits:
- name: train
num_examples: 180
- name: test
num_examples: 48
---
# Gesture Recognition Dataset
## Dataset Structure
- **Labels**: ['Good', 'Null', 'Thirsty', 'Bad', 'Me', 'Hungry']
- **Format**: Each record contains a 'label' and a 'batch' field
- **Batch Size**: 30 rows per batch (30 time steps)
- **Features**: 15 columns per row
- **Selection Method**: cosine_similarity - Files selected based on similarity to majority pattern
## Column Information
Each row in a batch contains 15 values in this order:
1. Timestamp - Timestamp
2. F1 - Flex sensor 1
3. F2 - Flex sensor 2
4. F3 - Flex sensor 3
5. F4 - Flex sensor 4
6. F5 - Flex sensor 5
7. Acc_Fin_x - Accelerometer Fin x axis
8. Acc_Fin_y - Accelerometer Fin y axis
9. Acc_Fin_z - Accelerometer Fin z axis
10. Acc_Palm_x - Accelerometer Palm x axis
11. Acc_Palm_y - Accelerometer Palm y axis
12. Acc_Palm_z - Accelerometer Palm z axis
13. Acc_Arm_x - Accelerometer Arm x axis
14. Acc_Arm_y - Accelerometer Arm y axis
15. Acc_Arm_z - Accelerometer Arm z axis
## Data Format
```python
{
'label': 'gesture_name', # One of: ['Good', 'Null', 'Thirsty', 'Bad', 'Me', 'Hungry']
'batch': [
[Timestamp,F1, F2, F3, F4, F5, Acc_Fin_x, Acc_Fin_y, Acc_Fin_z, Acc_Palm_x, Acc_Palm_y, Acc_Palm_z, Acc_Arm_x, Acc_Arm_y, Acc_Arm_z], # Row 1
[Timestamp,F1, F2, F3, F4, F5, Acc_Fin_x, Acc_Fin_y, Acc_Fin_z, Acc_Palm_x, Acc_Palm_y, Acc_Palm_z, Acc_Arm_x, Acc_Arm_y, Acc_Arm_z], # Row 2
... # 30 rows total
]
}
```
## Sensors
- **F1-F5**: Flex sensors measuring finger bend (5 sensors)
- **Acc_Fin**: Accelerometer on finger (x, y, z axes)
- **Acc_Palm**: Accelerometer on palm (x, y, z axes)
- **Acc_Arm**: Accelerometer on arm (x, y, z axes)
## Data Quality
Files were selected using cosine_similarity to ensure the most representative samples for each gesture class.
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